# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Loss functions."""

import torch
import torch.nn as nn

from utils.metrics import bbox_iou
from utils.torch_utils import de_parallel

def smooth_BCE(eps=0.1):
    """Returns label smoothing BCE targets for reducing overfitting; pos: `1.0 - 0.5*eps`, neg: `0.5*eps`. For details see https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441"""
    return 1.0 - 0.5 * eps, 0.5 * eps


class BCEBlurWithLogitsLoss(nn.Module):
    # BCEwithLogitLoss() with reduced missing label effects.
    def __init__(self, alpha=0.05):
        """Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing
        parameter.
        """
        super().__init__()
        self.loss_fcn = nn.BCEWithLogitsLoss(reduction="none")  # must be nn.BCEWithLogitsLoss()
        self.alpha = alpha

    def forward(self, pred, true):
        """Computes modified BCE loss for YOLOv5 with reduced missing label effects, taking pred and true tensors,
        returns mean loss.
        """
        loss = self.loss_fcn(pred, true)
        pred = torch.sigmoid(pred)  # prob from logits
        dx = pred - true  # reduce only missing label effects
        # dx = (pred - true).abs()  # reduce missing label and false label effects
        alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
        loss *= alpha_factor
        return loss.mean()



class FocalLoss(nn.Module):
    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
        """Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to
        'none'.
        """
        super().__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = loss_fcn.reduction
        self.loss_fcn.reduction = "none"  # required to apply FL to each element

    def forward(self, pred, true,weight = None):
        """Calculates the focal loss between predicted and true labels using a modified BCEWithLogitsLoss."""
        pred_sigmoid = torch.sigmoid(pred).clamp(min=1e-6, max=1 - 1e-6)
        loss = -torch.log(pred_sigmoid)*(1-pred_sigmoid)**self.gamma*true - torch.log(1-pred_sigmoid)*pred_sigmoid**self.gamma*(1-true)
        loss = torch.sum(loss, dim=-1)
        if weight is not None:
            loss = loss*weight

        return loss.sum()





class DFocalLoss(nn.Module):
    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
        """Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to
        'none'.
        """
        super().__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = loss_fcn.reduction
        self.loss_fcn.reduction = "none"  # required to apply FL to each element

    def forward(self, pred, true):
        """Calculates the focal loss between predicted and true labels using a modified BCEWithLogitsLoss."""
        loss = self.loss_fcn(pred, true)
        # p_t = torch.exp(-loss)
        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
        pred_prob = torch.sigmoid(pred)  # prob from logits
        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
        modulating_factor = (1.0 - p_t) ** self.gamma
        loss *= alpha_factor * modulating_factor
        loss = loss.sum()
        return loss


class QFocalLoss(nn.Module):
    # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
        """Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'."""
        super().__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = loss_fcn.reduction
        self.loss_fcn.reduction = "none"  # required to apply FL to each element

    def forward(self, pred, true):
        """Computes the focal loss between `pred` and `true` using BCEWithLogitsLoss, adjusting for imbalance with
        `gamma` and `alpha`.
        """
        loss = self.loss_fcn(pred, true)

        pred_prob = torch.sigmoid(pred)  # prob from logits
        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
        modulating_factor = torch.abs(true - pred_prob) ** self.gamma
        loss *= alpha_factor * modulating_factor

        if self.reduction == "mean":
            return loss.mean()
        elif self.reduction == "sum":
            return loss.sum()
        else:  # 'none'
            return loss

class ComputeLoss:
    sort_obj_iou = False

    # Compute losses
    def __init__(self, model, autobalance=False):
        """Initializes ComputeLoss with model and autobalance option, autobalances losses if True."""
        device = next(model.parameters()).device  # get model device
        cls_pw = 1.0
        obj_pw = 1.0
        label_smoothing = 0.0
        fl_gamma = 1.5


        self.lambda_box = 1.0
        self.lambda_obj = 1.0
        self.lambda_cls = 0.5
        self.anchor_t = 4.0

        # Define criteria
        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([cls_pw], device=device))
        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([obj_pw], device=device))
        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
        self.cp, self.cn = smooth_BCE(label_smoothing)  # positive, negative BCE targets

        g = fl_gamma  # focal loss gamma
        '''
        if fl_gamma > 0:
            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
        '''
        BCEcls = FocalLoss(BCEcls, g)
        BCEobj = DFocalLoss(BCEobj, g)

        m = de_parallel(model).model[-1]  # Detect() module

        #m = model

        #self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02])  # P3-P7
        self.balance = {3: [1.0, 1.0, 1.0]}.get(m.nl, [1.0, 1.0, 1.0, 1.0, 1.0])  # P3-P7

        #self.box_balance = {3: [2.0, 1.0, 1.0]}.get(m.nl, [2.0, 1.0, 0.5,0.5, 0.5])  # P3-P7
        self.box_balance = {3: [1.0, 1.0, 1.0]}.get(m.nl, [1.0, 1.0, 1.0,1.0, 1.0])  # P3-P7


        self.ssi = list(m.stride).index(16) if autobalance else 0  # stride 16 index

        self.BCEcls, self.BCEobj, self.gr,  self.autobalance = BCEcls, BCEobj, 1.0,  autobalance
        self.na = m.na  # number of anchors
        self.nc = m.nc  # number of classes
        self.nl = m.nl  # number of layers
        self.anchors = m.anchors
        self.device = device

    def __call__(self, p, targets):  # predictions, targets
        """Performs forward pass, calculating class, box, and object loss for given predictions and targets."""
        lcls = torch.zeros(1, device=self.device)  # class loss
        lbox = torch.zeros(1, device=self.device)  # box loss
        lobj = torch.zeros(1, device=self.device)  # object loss
        tcls, tbox, indices, anchors = self.build_targets(p, targets)  # targets



        box_size = 0
        obj_size = 0

        for i, pi in enumerate(p):  # layer index, layer predictions
            b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
            tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device)  # target obj

            n = b.shape[0]  # number of targets
            if n:
                # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1)  # faster, requires torch 1.8.0
                pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1)  # target-subset of predictions

                # Regression
                pxy = pxy.sigmoid() * 2 - 0.5
                pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
                pbox = torch.cat((pxy, pwh), 1)  # predicted box


                #area = torch.sqrt(tbox[i][:,2]*tbox[i][:,3])/8.0
                #area = area.clamp(0.7, 1.2)
                iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze()  # iou(prediction, target)

                iou = iou.clamp(min=1e-6, max=1.0)

                #iou = wasserstein_loss(pbox, tbox[i])*0.5+0.5*iou

                #lbox += (1.0 - iou).mean()  # iou loss
                box_size += iou.size(0)
                lbox +=  (1.0 - iou).sum()
                #lbox+= wasserstein_loss(pbox, tbox[i]).mean()

                # Objectness
                iou = iou.detach().clamp(0).type(tobj.dtype)


                if self.sort_obj_iou:
                    j = iou.argsort()
                    b, a, gj, gi, obj_lb = b[j], a[j], gj[j], gi[j], iou[j]

                if self.gr < 1:
                    iou = (1.0 - self.gr) + self.gr * iou

                tobj[b, a, gj, gi] = iou  # iou ratio
                obj_size +=iou.sum()

                # Classification
                if self.nc > 1:  # cls loss (only if multiple classes)
                    t = torch.full_like(pcls, self.cn, device=self.device)  # targets
                    t[range(n), tcls[i]] = self.cp
                    lcls += self.BCEcls(pcls, t, iou)  # BCE

            obji = self.BCEobj(pi[..., 4], tobj)
            lobj += obji # obj loss

            if self.autobalance:
                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
        if self.autobalance:
            self.balance = [x / self.balance[self.ssi] for x in self.balance]

        lbox = lbox/box_size*self.lambda_box
        lobj = lobj/obj_size*self.lambda_obj
        lcls = lcls /obj_size * self.lambda_cls
        #lbox *= self.lambda_box
        #lobj *= self.lambda_obj
        #lcls *= self.lambda_cls
        bs = tobj.shape[0]  # batch size
        return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()


    def build_targets(self, p, targets):
        """Prepares model targets from input targets (image,class,x,y,w,h) for loss computation, returning class, box,
        indices, and anchors.
        """
        na, nt = self.na, targets.shape[0]  # number of anchors, targets
        tcls, tbox, indices, anch = [], [], [], []

        gain = torch.ones(7, device=self.device)  # normalized to gridspace gain

        ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt)  # num_anchor x num_gt


        targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2)
        # img_id, class, x, y, w, h, anchor_id gain used for x,y,w,h to real szie


        g = 0.5  # bias
        off = (
            torch.tensor(
                [
                    [0, 0],
                    [1, 0],
                    [0, 1],
                    [-1, 0],
                    [0, -1],  # j,k,l,m
                    # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
                ],
                device=self.device,
            ).float()
            * g
        )  # offsets Define the offsets in each grid direction (j, k, l, m) (left, up, right, down)

        for i in range(self.nl):
            anchors, shape = self.anchors[i], p[i].shape

            gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]]  # xyxy gain features (w,h,w,h)

            # Match targets to anchors

            t = targets * gain  # shape(3,n,7) gt


            if nt:
                # Matches

                r = t[..., 4:6] / anchors[:, None]  # wh ratio

                j = torch.max(r, 1 / r).max(2)[0] < self.anchor_t  # compare

                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                t = t[j]  # filter

                # Offsets
                gxy = t[:, 2:4]  # grid xy


                gxi = gain[[2, 3]] - gxy  # inverse


                j, k = ((gxy % 1 < g) & (gxy > 1)).T
                l, m = ((gxi % 1 < g) & (gxi > 1)).T


                j = torch.stack((torch.ones_like(j), j, k, l, m))

                t = t.repeat((5, 1, 1))[j]

                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
            else:
                t = targets[0]
                offsets = 0



            # Define
            bc, gxy, gwh, a = t.chunk(4, 1)  # (image, class), grid xy, grid wh, anchors

            a, (b, c) = a.long().view(-1), bc.long().T  # anchors, image, class

            gij = (gxy - offsets).long()
            gi, gj = gij.T  # grid indices

            # Append
            indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1)))  # image, anchor, grid

            tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
            anch.append(anchors[a])  # anchors
            tcls.append(c)  # class



        return tcls, tbox, indices, anch



if __name__ == '__main__':
    x1 = torch.randn(2, 3, 640, 640)


